Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Stability01:28

Stability

The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Concepts and Prototypes01:24

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Construction of Root Locus01:15

Construction of Root Locus

The construction of a root locus involves several key steps to analyze and visualize the behavior of a system's poles with varying gain. The number of branches in the root locus equals the number of closed-loop poles and is symmetrical about the real axis.
For positive gain values, the root locus exists on the real axis to the left of an odd number of finite open-loop poles or zeros. The root locus starts at the open-loop poles and traces the paths of the closed-loop poles as the gain increases.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluating RAG and Non-RAG Pipelines for Concept Discovery in Environmental Health Ontologies.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Single nuclei RNA-sequencing reveals transcriptional heterogeneity in the blastema of favorable histology Wilms tumor.

JCI insight·2026
Same author

Relational Graph Convolutional Network with BERT Embeddings for Ontology Relationship Classification.

Studies in health technology and informatics·2026
Same author

Humoral immunity and infection status of PLWH following vaccination after the BA.5/BF.7 wave.

Frontiers in microbiology·2026
Same author

Spatial Solvation Regulation by a Swollen Polymer Interphase Enables Ultrastable Sodium Metal Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Multi-omics profiling implicates gut microbiota-sphingolipid interplay in the neuroprotective effects of semaglutide on diabetic cognitive impairment.

Frontiers in microbiology·2026

Related Experiment Video

Updated: May 24, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Convergence to Steady State in LLM-Generated Ontological Concepts.

Naren Khatwani1, Lijing Wang1, Shmuel T Klein2

  • 1New Jersey Institute of Technology, USA.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Large Language Models (LLMs) can generate ontology concepts, but high temperatures increase errors. Lower temperatures lead to faster, more stable concept generation for Environmental Determinants of Health (EnDOH) ontologies.

Keywords:
LLM hallucinationLLM temperatureLLMsdeterminants of healthmedical ontologiesontology expansion

Related Experiment Videos

Last Updated: May 24, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Area of Science:

  • Computational Linguistics
  • Bioinformatics
  • Health Informatics

Background:

  • Large Language Models (LLMs) are increasingly used for automated ontology expansion.
  • LLM output quality is sensitive to sampling parameters, particularly 'temperature', which influences creativity versus factual accuracy.
  • High temperatures can lead to 'hallucinations' or irrelevant concept generation.

Purpose of the Study:

  • To investigate the relationship between LLM sampling temperature and the quality/consistency of generated ontology concepts.
  • To test hypotheses regarding superset relationships and convergence speed at different temperatures.
  • To analyze Environmental Determinants of Health (EnDOH) concepts using LLMs.

Main Methods:

  • LLM concept generation using concept-structured prompting across temperatures from 0.1 to 1.0.
  • Iterative execution of fixed prompts to assess convergence to a steady state (k-convergence).
  • Analysis of generated concepts for superset properties and convergence rates.

Main Results:

  • Contrary to the first hypothesis, higher temperatures did not consistently yield supersets of concepts generated at lower temperatures.
  • The second hypothesis was supported: lower temperatures demonstrated faster convergence to a steady state.
  • LLM sampling temperature significantly impacts concept generation stability and consistency.

Conclusions:

  • The 'temperature' parameter in LLMs does not guarantee a simple superset relationship for ontology expansion.
  • Lower sampling temperatures promote more stable and faster convergence in LLM-based concept generation.
  • Careful selection of LLM parameters is crucial for reliable ontology development, especially in specialized domains like EnDOH.